balancap/SSD-Tensorflow
SSD Tensorflow
Dieses Projekt ist ein TensorFlow-Framework zur Objekterkennung, das für das Training und die Bereitstellung von Single-Shot-MultiBox-Detector-Modellen (SSD) konzipiert ist. Es bietet ein Toolkit für das Training neuronaler Netze zur Implementierung der SSD-Architektur, um eine Objekterkennung in Bildern und Videos in Echtzeit zu erreichen.
Das Framework enthält eine dedizierte Daten-Pipeline zur Transformation von Objekterkennungs-Datensätzen in binäre Record-Formate, um Trainingsgeschwindigkeit und Performance zu steigern. Es bietet zudem Utilities zur Konvertierung von Modellgewichten zwischen verschiedenen Checkpoint-Formaten, um die Wiederverwendung vortrainierter Netzwerke zu erleichtern.
Das System deckt ein breites Spektrum an Funktionen ab, einschließlich Modell-Fine-Tuning auf benutzerdefinierten Datensätzen, Training zur Objekterkennung und Genauigkeitsevaluierung durch Messung von Precision- und Recall-Metriken.
Features
- Object Detection Models - Implements the Single Shot MultiBox Detector (SSD) architecture for fast and accurate real-time object localization.
- Object Detection - Implements a system to identify and locate multiple objects within images using neural networks.
- Real-Time Object Detection - Provides high-speed inference for identifying and locating multiple objects in live video streams.
- Detection Model Training - Provides the capability to build detection networks using custom datasets and optimizer settings.
- Neural Network Training Toolkits - Provides a comprehensive software suite for the iterative development, training, and optimization of detection models.
- TensorFlow Model Development - Offers a complete framework for designing, building, and training SSD models within the TensorFlow ecosystem.
- Dataset Preprocessing Tools - Includes utilities for converting raw object detection data into binary record formats for optimized TensorFlow ingestion.
- Model Fine-Tuning - Supports adapting pre-trained detection models to specific tasks by updating weights on new datasets.
- Object Detection Evaluators - Provides tools to measure model performance by comparing predicted bounding boxes against ground truth for precision and recall.
- Detection Accuracy Evaluators - Calculates precision and recall metrics to quantify the localization and classification accuracy of detection models.
- Vision Model Fine-Tuning - Allows adapting pre-trained detection networks to new specific tasks by updating weights on custom datasets.
- TFRecord Data Processing - Ships a data conversion utility for serializing object detection datasets into TFRecord binary format.
- Binary Record Converters - Provides a dedicated pipeline for transforming object detection datasets into binary record formats for faster training.
